Secondary production of the central rangeland region of the United States.

Rangelands are the dominant land use across a broad swath of central North America where they span a wide gradient, from <350 to >900 mm, in mean annual precipitation. Substantial efforts have examined temporal and spatial variation in aboveground net primary production (ANPP) to precipitation (PPT) across this gradient. In contrast, net secondary productivity (NSP, e.g., primary consumer production) has not been evaluated analogously. However, livestock production, which is a form of NSP or primary consumer production supported by primary production, is the dominant non-cultivated land use and an integral economic driver in these regions. Here, we used long-term (mean length = 19 years) ANPP and NSP data from six research sites across the Central Great Plains with a history of a conservative stocking to determine resource (i.e., PPT)-productivity relationships, NSP sensitivities to dry-year precipitation, and regional trophic efficiencies (e.g., NSP:ANPP ratio). PPT-ANPP relationships were linear for both temporal (site-based) and spatial (among site) gradients. The spatial PPT-NSP model revealed that PPT mediated a saturating relationship for NSP as sites became more mesic, a finding that contrasts with many plant-based PPT-ANPP relationships. A saturating response to high growing-season precipitation suggests biogeochemical rather than vegetation growth constraints may govern NSP (i.e., large herbivore production). Differential sensitivity in NSP to dry years demonstrated that the primary consumer production response heightened as sites became more xeric. Although sensitivity generally decreased with increasing precipitation as predicted from known PPT-ANPP relationships, evidence suggests that the dominant species' identity and traits influenced secondary production efficiency. Non-native northern mixed-grass prairie was outperformed by native Central Great Plains rangeland in sensitivity to dry years and efficiency in converting ANPP to NSP. A more comprehensive understanding of the mechanisms leading to differences in producer and consumer responses will require multisite experiments to assess biotic and abiotic determinants of multi-trophic level efficiency and sensitivity.

ASAS-NANP symposium: mathematical modeling in animal nutrition-Making sense of big data and machine learning: how open-source code can advance training of animal scientists.

Advancements in precision livestock technology have resulted in an unprecedented amount of data being collected on individual animals. Throughout the data analysis chain, many bottlenecks occur, including processing raw sensor data, integrating multiple streams of information, incorporating data into animal growth and nutrition models, developing decision support tools for producers, and training animal science students as data scientists. To realize the promise of precision livestock management technologies, open-source tools and tutorials must be developed to reduce these bottlenecks, which are a direct result of the tremendous time and effort required to create data pipelines from scratch. Open-source programming languages (e.g., R or Python) can provide users with tools to automate many data processing steps for cleaning, aggregating, and integrating data. However, the steps from data collection to training artificial intelligence models and integrating predictions into mathematical models can be tedious for those new to statistical programming, with few examples pertaining to animal science. To address this issue, we outline how open-source code can help overcome many of the bottlenecks that occur in the era of big data and precision livestock technology, with an emphasis on how routine use and publication of open-source code can help facilitate training the next generation of animal scientists. In addition, two case studies are presented with publicly available data and code to demonstrate how open-source tutorials can be utilized to streamline data processing, train machine learning models, integrate with animal nutrition models, and facilitate learning. The National Animal Nutrition Program focuses on providing research-based data on animal performance and feeding strategies. Open-source data and code repositories with examples specific to animal science can help create a reinforcing mechanism aimed at advancing animal science research.

Livestock production is undergoing a new revolution of incorporating advanced technology to inform animal management. As more and more technologies come to market, new challenges arise with developing a workforce trained to handle big datasets generated from these technologies and turning datasets into insight for livestock producers. This can be especially challenging as multiple data streams ranging from climate and weather information to real-time metrics on animal performance need to be efficiently processed and incorporated into animal production models. Open-source code is one possible solution to these challenges because it is designed to be made publicly available so any user can view, alter, and improve upon existing code. This paper aims to highlight how open-source code can help address many of the challenges of precision livestock technology, including efficient data processing, data integration, development of decision tools, and training of future animal scientists. In addition, the need for open-source tutorials and datasets specific to animal science are included to help facilitate greater adoption of open science.

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production.

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

Interest and investment in precision technologies are growing within the livestock sector. Though these technologies offer many promises of increased efficiency and reduced inputs, there is a need to assess the opportunities and challenges associated with precision technology implementation in livestock production systems. In this review, precision livestock measurement and management tools are explained in the context of a logical and iterative five-step process that highlights the need for systems computer modeling to realize anticipated benefits from these technologies and avoid unintended consequences. This review includes key case studies to highlight past challenges and current opportunities within operations that house animals in a central area or building with sufficient infrastructure (confined livestock production systems) and other operation settings that utilize large grasslands that contain far less infrastructure (extensive livestock production systems). The key to precision livestock management success is training the next generation of animal scientists in computer modeling, precision technologies, computer programming, and data science while still being grounded in traditional animal science principles.

Protein Supplementation and Grazing Behavior for Cows on Differing Late-Season Rangeland Grazing Systems.

The objective was to determine if low- or high-residual feed intake (LRFI or HRFI, n = 24 for each) Hereford × Angus cows on continuously or rotationally grazed rangeland altered their grazing behavior when provided a protein supplement in late autumn. Treatments included continuously grazed, control (CCON, n = 12); continuously grazed, supplemented (CTRT, n = 12); rotationally grazed, control (RCON, n = 12); and rotationally grazed, supplemented pastures (RTRT, n = 12). Cows in each treatment had grazing time (GT), resting time (RT), and walking time (WLK) measured for 2 years with accelerometers. Bite rate (BR) was also measured. Time distributions of GT and RT differed by year (p < 0.05), being influenced by colder temperatures in 2016. Cattle in 2016 spent more time grazing during early morning and late evening (p < 0.05) and rested more during the day (p < 0.05). In 2017, cattle in the CCON treatment walked more (p < 0.05) during early morning time periods than did the CTRT cattle, indicative of search grazing. All supplemented cattle had greater BR (p < 0.05) than control cattle in 2017. Cattle with increased nutritional demands alter grazing behavior in a compensatory fashion when grazing late-season rangelands.

Grazing behavior and production for lactating cows differing in residual feed intake while grazing spring and summer rangeland.

The objectives were to determine if previously classified, efficient (LRFI, low-residual-feed intake, n = 12 × 2 yr) vs. inefficient (HRFI, high-residual-feed intake, n = 12 × 2 yr) lactating 2-yr-old Hereford × Angus cows differed in grazing behavior, body weight (BW), body condition score (BCS), and calf weaning weight while grazing rugged rangeland pastures. Cows were fitted with grazing halters containing both an accelerometer and a global positioning system (GPS) data logger during June 14 to July 4, 2016, August 2 to 25, 2016, May 23 to June 12, 2017, and August 5 to 28, 2017. GPS data were recorded at 7-min intervals in 2016 and 4-min intervals in 2017 and accelerometer data recorded at 25 times/s. Grazing time (GT), resting, walking, bite rate (BR), daily travel distance (DTD), elevation, and slope were analyzed with a mixed model that included fixed effects of RFI group, day, and RFI group × day and cow within treatment as the random effect. Cow BW, BCS, and calf weaning weight were analyzed by analysis of variance with treatment as the main effect. There were no differences (P > 0.10) due to RFI detected for BW, BCS, or calf weaning weights. During periods of mild heat load (MHL), HRFI cows spent more (P < 0.05) time resting during the day at lower elevations (P < 0.05) than LRFI cows. During a 6-d period in spring with only 2 h MHL, HRFI cows grazed 1.7 h/d longer than LRFI cows (P < 0.05); commencing grazing earlier in the morning and extending the grazing bout later. During the summer with > MHL, LRFI cows grazed more than HRFI cows 18% of the time (P < 0.10). The HRFI cows had greater GT than LRFI cows only 3% of the time (P < 0.10) during summer. There was no difference (P > 0.10) in BR between HRFI and LRFI cattle. The DTD tended (P < 0.10) to be greater for LRFI cattle during summer 2017. Over all sample periods, HRFI had greater walking than LRFI 15% of the time and LRFI exceeded HRFI cattle for walking 3% of the time (P < 0.10). The greater walking for HRFI was assumed to be associated with more search grazing. Metabolic heat load on hot summer days for HRFI cattle is presumed to have contributed to differences observed in grazing behavior. These results suggest that lactating cows with low-RFI phenotypes appear to be better adapted to grazing rugged rangelands in late summer during periods of MHL.